What needed to be understood.
The unrestricted tree fit the training data extremely well but generalized poorly. The task required diagnosing overfitting and selecting parameters using validation rather than training fit.
How the analysis was built.
- Built a full regression tree and evaluated its holdout performance.
- Tested combinations of maximum depth, minimum leaf size, and minimum split size.
- Used five-fold cross-validation across 216 parameter combinations.
- Compared variable importance and predictive error before and after tuning.
What the evidence showed.
- Vehicle age, kilometers driven, quarterly tax, and automatic air conditioning were the most influential specifications.
- The unrestricted tree overfit the training data.
- The selected configuration reduced holdout RMSE from roughly 1,309 to 1,255.
What should happen next.
- Select tree complexity with cross-validation rather than visual fit to training data.
- Monitor performance by vehicle segment and price range.
- Add condition, service history, location, and market-demand variables for stronger pricing accuracy.
What this project does—and does not—prove.
The selected dataset contains a limited set of vehicle characteristics and represents an academic prediction exercise rather than a production appraisal system.